Geographical Exploration and Analysis Extended to Textual Content (Short Paper)

Détails

Ressource 1Télécharger: LIPIcs-GISCIENCE-2018-23.pdf (598.81 [Ko])
Etat: Public
Version: Final published version
ID Serval
serval:BIB_D29E670710D6
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Titre
Geographical Exploration and Analysis Extended to Textual Content (Short Paper)
Titre de la conférence
10th International Conference on Geographic Information Science (GIScience 2018)
Auteur(s)
Ceré R., Egloff M., Bavaud F.
Editeur
Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany
Organisation
10th International Conference on Geographic Information Science (GIScience 2018)
Adresse
Melbourne, Australia
ISBN
978-3-95977-083-5
ISSN
1868-8969
Statut éditorial
Publié
Date de publication
2018
Peer-reviewed
Oui
Editeur scientifique
Winter S., Griffin A., Sester M.
Volume
114
Série
Leibniz International Proceedings in Informatics (LIPIcs)
Pages
23:1-23:7
Langue
anglais
Résumé
Textual and socio-economical regional features can be integrated and merged by linearly combining the between-regions corresponding dissimilarities. The scheme accommodates for various squared Euclidean socio-economical and textual dissimilarities (such as chi2 or cosine dissimilarities derived from document-term matrix or topic modelling). Also, spatial configuration of the regions can be represented by a weighted unoriented network whose vertex weights match the relative importance of regions. Association between the network and the dissimilarities expresses in the multivariate spatial autocorrelation index δ, generalizing Moran’s I, whose local version can be cartographied. Our case study bears on the Wikipedia notices and socio-economic profiles for the 2251 Swiss municipalities, whose weights (socio-economical or textual) can be freely chosen.
Mots-clé
Spatial autocorrelation, Weighted spatial network, Document-term matrix, Multivariate features, Soft clustering
Création de la notice
10/08/2018 14:27
Dernière modification de la notice
20/08/2019 16:52
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